
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	def __init__(			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     NestedDataStructureLike[PathLike]			,				SCREAMING_SNAKE_CASE_    :     Optional[NamedSplit] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[Features] = None			,				SCREAMING_SNAKE_CASE_    :     str = None			,				SCREAMING_SNAKE_CASE_    :     bool = False			,				SCREAMING_SNAKE_CASE_    :     bool = False			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[int] = None			,				**SCREAMING_SNAKE_CASE_    :     int			,				)			->	Dict:
					super().__init__(
					    SCREAMING_SNAKE_CASE_			,				split=SCREAMING_SNAKE_CASE_			,				features=SCREAMING_SNAKE_CASE_			,				cache_dir=SCREAMING_SNAKE_CASE_			,				keep_in_memory=SCREAMING_SNAKE_CASE_			,				streaming=SCREAMING_SNAKE_CASE_			,				num_proc=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			,				)
					lowercase_	    =   field
					lowercase_	    =   path_or_paths if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) else {self.split: path_or_paths}
					lowercase_	    =   Json(
					    cache_dir=SCREAMING_SNAKE_CASE_			,				data_files=SCREAMING_SNAKE_CASE_			,				features=SCREAMING_SNAKE_CASE_			,				field=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			,				)




	def 		_lowercase    (			self    :     Dict			)			->	Optional[int]:
					# Build iterable dataset
					if self.streaming:
									lowercase_	    =   self.builder.as_streaming_dataset(split=self.split			)
					# Build regular (map-style) dataset
					else:
									lowercase_	    =   None
									lowercase_	    =   None
									lowercase_	    =   None
									lowercase_	    =   None

									self.builder.download_and_prepare(
									    download_config=SCREAMING_SNAKE_CASE_			,				download_mode=SCREAMING_SNAKE_CASE_			,				verification_mode=SCREAMING_SNAKE_CASE_			,				base_path=SCREAMING_SNAKE_CASE_			,				num_proc=self.num_proc			,				)
									lowercase_	    =   self.builder.as_dataset(
									    split=self.split			,				verification_mode=SCREAMING_SNAKE_CASE_			,				in_memory=self.keep_in_memory			)
					return dataset

class 					lowercase__:



	"""simple docstring"""




	def __init__(			self    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Dataset			,				SCREAMING_SNAKE_CASE_    :     Union[PathLike, BinaryIO]			,				SCREAMING_SNAKE_CASE_    :     Optional[int] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[int] = None			,				**SCREAMING_SNAKE_CASE_    :     List[str]			,				)			->	int:
					if num_proc is not None and num_proc <= 0:
									raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''			)

					lowercase_	    =   dataset
					lowercase_	    =   path_or_buf
					lowercase_	    =   batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
					lowercase_	    =   num_proc
					lowercase_	    =   '''utf-8'''
					lowercase_	    =   to_json_kwargs
	def 		_lowercase    (			self    :     List[Any]			)			->	int:
					lowercase_	    =   self.to_json_kwargs.pop('''path_or_buf'''			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   self.to_json_kwargs.pop('''orient'''			,				'''records'''			)
					lowercase_	    =   self.to_json_kwargs.pop('''lines'''			,				True if orient == '''records''' else False			)
					lowercase_	    =   self.to_json_kwargs.pop('''index'''			,				False if orient in ['''split''', '''table'''] else True			)
					lowercase_	    =   self.to_json_kwargs.pop('''compression'''			,				SCREAMING_SNAKE_CASE_			)

					if compression not in [None, "infer", "gzip", "bz2", "xz"]:
									raise NotImplementedError(f'''`datasets` currently does not support {compression} compression'''			)

					if isinstance(self.path_or_buf			,				(str, bytes, os.PathLike)			):
									with fsspec.open(self.path_or_buf			,				'''wb'''			,				compression=SCREAMING_SNAKE_CASE_			) as buffer:
													lowercase_	    =   self._write(file_obj=SCREAMING_SNAKE_CASE_			,				orient=SCREAMING_SNAKE_CASE_			,				lines=SCREAMING_SNAKE_CASE_			,				index=SCREAMING_SNAKE_CASE_			,				**self.to_json_kwargs			)
					else:
									if compression:
													raise NotImplementedError(
													    f'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
													    ''' was passed. Please provide a local path instead.'''			)
									lowercase_	    =   self._write(
									    file_obj=self.path_or_buf			,				orient=SCREAMING_SNAKE_CASE_			,				lines=SCREAMING_SNAKE_CASE_			,				index=SCREAMING_SNAKE_CASE_			,				**self.to_json_kwargs			)
					return written
	def 		_lowercase    (			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			)			->	List[Any]:
					lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_	    =   args

					lowercase_	    =   query_table(
					    table=self.dataset.data			,				key=slice(SCREAMING_SNAKE_CASE_			,				offset + self.batch_size			)			,				indices=self.dataset._indices			,				)
					lowercase_	    =   batch.to_pandas().to_json(
					    path_or_buf=SCREAMING_SNAKE_CASE_			,				orient=SCREAMING_SNAKE_CASE_			,				lines=SCREAMING_SNAKE_CASE_			,				index=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
					if not json_str.endswith('''\n'''			):
									json_str += "\n"
					return json_str.encode(self.encoding			)




	def 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     BinaryIO			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				**SCREAMING_SNAKE_CASE_    :     str			,				)			->	int:
					lowercase_	    =   0

					if self.num_proc is None or self.num_proc == 1:
									for offset in logging.tqdm(
									    range(0			,				len(self.dataset			)			,				self.batch_size			)			,				unit='''ba'''			,				disable=not logging.is_progress_bar_enabled()			,				desc='''Creating json from Arrow format'''			,				):
													lowercase_	    =   self._batch_json((offset, orient, lines, index, to_json_kwargs)			)
													written += file_obj.write(SCREAMING_SNAKE_CASE_			)
					else:
									lowercase_    ,      lowercase_	    =   len(self.dataset			), self.batch_size
									with multiprocessing.Pool(self.num_proc			) as pool:
													for json_str in logging.tqdm(
													    pool.imap(
													        self._batch_json			,				[(offset, orient, lines, index, to_json_kwargs) for offset in range(0			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)]			,				)			,				total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,				unit='''ba'''			,				disable=not logging.is_progress_bar_enabled()			,				desc='''Creating json from Arrow format'''			,				):
																	written += file_obj.write(SCREAMING_SNAKE_CASE_			)

					return written











'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase									=   logging.get_logger(__name__)

lowerCAmelCase									=   {
    """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class 		lowerCamelCase	(   _A							):
	snake_case_   = "gpt_neox"



	def __init__( self		,   a_=50_432		,   a_=6_144		,   a_=44		,   a_=64		,   a_=24_576		,   a_="gelu"		,   a_=0.25		,   a_=10_000		,   a_=0.0		,   a_=0.0		,   a_=0.1		,   a_=2_048		,   a_=0.02		,   a_=1e-5		,   a_=True		,   a_=0		,   a_=2		,   a_=False		,   a_=True		,   a_=None		,   **a_		,   ):
			super().__init__(bos_token_id=a_		,   eos_token_id=a_		,   **a_						)
			lowerCAmelCase  :     Dict       						= vocab_size
			lowerCAmelCase  :     str       						= max_position_embeddings
			lowerCAmelCase  :     int       						= hidden_size
			lowerCAmelCase  :     Optional[Any]       						= num_hidden_layers
			lowerCAmelCase  :     int       						= num_attention_heads
			lowerCAmelCase  :     Any       						= intermediate_size
			lowerCAmelCase  :     Tuple       						= hidden_act
			lowerCAmelCase  :     Any       						= rotary_pct
			lowerCAmelCase  :     str       						= rotary_emb_base
			lowerCAmelCase  :     Tuple       						= attention_dropout
			lowerCAmelCase  :     Optional[int]       						= hidden_dropout
			lowerCAmelCase  :     int       						= classifier_dropout
			lowerCAmelCase  :     str       						= initializer_range
			lowerCAmelCase  :     str       						= layer_norm_eps
			lowerCAmelCase  :     List[Any]       						= use_cache
			lowerCAmelCase  :     Dict       						= tie_word_embeddings
			lowerCAmelCase  :     Any       						= use_parallel_residual
			lowerCAmelCase  :     Union[str, Any]       						= rope_scaling
			self._rope_scaling_validation()

			if self.hidden_size % self.num_attention_heads != 0:
					raise ValueError(
					    "The hidden size is not divisble by the number of attention heads! Make sure to update them!"						)





	def 	_lowerCamelCase     ( self						):
			if self.rope_scaling is None:
					return

			if not isinstance(self.rope_scaling		,   a_						) or len(self.rope_scaling						) != 2:
					raise ValueError(
					    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
					    F'''got {self.rope_scaling}'''						)
			lowerCAmelCase  :     int       						= self.rope_scaling.get("type"		,   a_						)
			lowerCAmelCase  :     int       						= self.rope_scaling.get("factor"		,   a_						)
			if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
					raise ValueError(
					    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''						)
			if rope_scaling_factor is None or not isinstance(a_		,   a_						) or rope_scaling_factor <= 1.0:
					raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''						)



